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  <div class="section" id="numpy-nancumsum">
<h1>numpy.nancumsum<a class="headerlink" href="#numpy-nancumsum" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="numpy.nancumsum">
<code class="sig-prename descclassname">numpy.</code><code class="sig-name descname">nancumsum</code><span class="sig-paren">(</span><em class="sig-param">a</em>, <em class="sig-param">axis=None</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/numpy/numpy/blob/v1.18.1/numpy/lib/nanfunctions.py#L727-L790"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#numpy.nancumsum" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the cumulative sum of array elements over a given axis treating Not a
Numbers (NaNs) as zero.  The cumulative sum does not change when NaNs are
encountered and leading NaNs are replaced by zeros.</p>
<p>Zeros are returned for slices that are all-NaN or empty.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.12.0.</span></p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">array_like</span></dt><dd><p>Input array.</p>
</dd>
<dt><strong>axis</strong><span class="classifier">int, optional</span></dt><dd><p>Axis along which the cumulative sum is computed. The default
(None) is to compute the cumsum over the flattened array.</p>
</dd>
<dt><strong>dtype</strong><span class="classifier">dtype, optional</span></dt><dd><p>Type of the returned array and of the accumulator in which the
elements are summed.  If <a class="reference internal" href="numpy.dtype.html#numpy.dtype" title="numpy.dtype"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dtype</span></code></a> is not specified, it defaults
to the dtype of <em class="xref py py-obj">a</em>, unless <em class="xref py py-obj">a</em> has an integer dtype with a
precision less than that of the default platform integer.  In
that case, the default platform integer is used.</p>
</dd>
<dt><strong>out</strong><span class="classifier">ndarray, optional</span></dt><dd><p>Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output
but the type will be cast if necessary. See <em class="xref py py-obj">ufuncs-output-type</em> for
more details.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>nancumsum</strong><span class="classifier">ndarray.</span></dt><dd><p>A new array holding the result is returned unless <em class="xref py py-obj">out</em> is
specified, in which it is returned. The result has the same
size as <em class="xref py py-obj">a</em>, and the same shape as <em class="xref py py-obj">a</em> if <em class="xref py py-obj">axis</em> is not None
or <em class="xref py py-obj">a</em> is a 1-d array.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="numpy.cumsum.html#numpy.cumsum" title="numpy.cumsum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.cumsum</span></code></a></dt><dd><p>Cumulative sum across array propagating NaNs.</p>
</dd>
<dt><a class="reference internal" href="numpy.isnan.html#numpy.isnan" title="numpy.isnan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isnan</span></code></a></dt><dd><p>Show which elements are NaN.</p>
</dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nancumsum</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="go">array([1])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nancumsum</span><span class="p">([</span><span class="mi">1</span><span class="p">])</span>
<span class="go">array([1])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nancumsum</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">])</span>
<span class="go">array([1.,  1.])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nancumsum</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">array([1.,  3.,  6.,  6.])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nancumsum</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go">array([[1.,  2.],</span>
<span class="go">       [4.,  2.]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nancumsum</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="go">array([[1.,  3.],</span>
<span class="go">       [3.,  3.]])</span>
</pre></div>
</div>
</dd></dl>

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